Chin detecting method, chin detecting system and chin detecting program for a chin of a human face

ABSTRACT

A chin detecting method is provided. After detecting a human face and setting a chin detecting window at a lower part of the image, an edge strength distribution is calculated within the chin detecting window and pixels having an edge strength with a threshold value or more are detected based on the edge strength distribution. Then an approximated curve is obtained to most match a distribution of each of the detected pixels and a lowermost part of the approximated curve is identified as the lower base of the chin of the human face. Thereby the chin lower base of the human face can be detected automatically, accurately and quickly.

RELATED APPLICATIONS

This application claims priority to Japanese Patent Application No.2003-407911 filed Dec. 5, 2003 which is hereby expressly incorporated byreference herein in its entirety.

BACKGROUND

1. Technical Field

The present invention concerns pattern recognition and objectrecognition technologies, and more specifically, the invention relatesto a chin detecting method, a chin detecting system and a chin detectingprogram for accurately detecting the location of the chin of a humanface from an image of the human face.

2. Related Art

With recent advancements in pattern recognition technologies andinformation processors such as computers, the recognition accuracy oftext and sound has been dramatically improved. However, in the patternrecognition of an image of a human, an object, the landscape and so on,e.g., an image scanned from a digital still camera and the like, it isstill difficult to accurately and quickly identify whether a human faceis visible in the image or not.

However, automatically and accurately identifying whether a human faceis visible in the image or not and who the human is with a computer hasbeen extremely important to establish a living body recognition process,improve security, accelerate criminal investigations, speed up imagedata reduction and retrieval, and so on. In this regard, many proposalshave been made.

In JP-A-9-50528, the existence of a flesh color area is first determinedin an input image, and the mosaic size is automatically determined inthe flesh color area to convert a candidate area into a mosaic pattern.Then, the existence of human face is determined by calculating aproximity from a human face dictionary and mis-extraction due to theinfluence of background and like can be reduced by segmenting the humanface. Thereby, the human face can be automatically and effectivelydetected from the image.

In JP-A-8-77334, extraction of the feature point of a face image to beused for distinguishing each individual and group (for example, anethnic group) is automatically and easily performed at a high speed byusing a predetermined algorithm.

Incidentally, with regard to a required photo of a human face (which isa face image) for a passport, photo ID card and the like, manyphotographic pose requirements are set in detail such as the size of thephoto, the direction to which the human face faces, and the size andlocation of the human face within the photograph.

For example, not to mention the requirements for no background and noaccessories such as a hat, there are detailed regulations requiring thatthe human face point to the front, that the human face be located in thecenter of photo, that the chin of the face be located within a specificarea relative to the lower frame of the photo, and so on. In principle,a photo of a face image that is outside of the regulations is notadopted.

However, it is impractical to retake a photo simply because the size andlocation of the face in the photo is slightly out of regulation,although it may be rational if the human face is not facing the front orif an accessory such as a hat is worn. This causes a problem of imposingconsiderable labor and cost on a user.

For this reason, a method of solving the above problems has beenexamined by using digital image processing which has developedsignificantly in recent years.

For example, the following method has been examined to solve suchproblems. First, digital image data of the human face image is directlyobtained by a digital still camera or the like using an electronic imagepickup device such as a CCD or a CMOS. On the other hand, the digitalimage data may be acquired from an analog photo (silver salt photo) ofthe human face by using an electronic optical image scanner. Once thedigital image data is obtained, easy image processing such as zoomingin/out and moving is performed on the digital image data by using animage processing system comprising a general-purpose computer such as aPC and general-purpose software without damaging the innatecharacteristics of the human face in the photo.

Although it is possible to manually perform the above process by usinggeneral-purpose I/O devices such as a mouse, a keyboard and a monitorwhen the number of images to be processed is small, it becomes necessaryto perform the process automatically by using the aforementionedconventional techniques when the number of images increases.

To realize the automation of image processing for a human face, however,it is necessary to accurately recognize a face outline, and especially achin outline of the human face which cannot be accurately scanned inmany cases with only a conventional edge detection filter due tolighting conditions, features of the person in the photo and otherconditions that are present while taking the picture.

For example, the chin becomes vaguely-outlined depending on the presenceof scattered light and the direction of the lighting. Also, a relativelystrong edge is often detected between lips and the lower base of thechin depending on the facial features of the subject and between thecollar and neck depending on the clothes worn. In many cases a strongeredge is generated at the wrinkles of the neck than at the chin outlinedepending on the age and shape of the subject, and this edge issometimes mistakenly detected as the chin outline.

The present invention has been achieved to solve the aforementionedproblems. Therefore, one object of the invention is to provide a novelchin detecting method, a chin detecting system and a chin detectingprogram capable of detecting a robust chin lower base by accurately andquickly detecting an outline of the chin, which is difficult to detectfrom a face image.

SUMMARY

A chin detecting method for detecting a lower base of a chin of a humanface from an image with the human face included therein according toAspect 1 is characterized in that: after detecting a face image of anarea including both eyes and the lips of the human face but notincluding the chin and after setting a chin detecting window with a sizeincluding the chin of the human face at a lower part of the detectedface image, a pixel having an edge strength with a threshold value ormore is detected based on an edge strength distribution by calculatingthe edge strength distribution within the chin detecting window; andthen an approximated curve is obtained to match a distribution of eachof the detected pixels and a lowermost part of the approximated curve isidentified as the lower base of the chin of the human face.

In the invention as described above, after selecting a part with a highpossibility of including the chin of a human face and setting the chindetecting window at that part, the edge strength distribution within thechin detecting window is calculated. In other words, an outlineincluding the chin lower base generally has a higher edge strength thanthat of the periphery of the outline due to a sharp contrast between theoutline and the periphery thereof. Consequently, it becomes possible toeasily and reliably select a candidate area to be the outline includingthe chin lower base that should be included in the chin detecting windowby calculating the edge strength distribution within the chin detectingwindow.

Next, when the edge strength distribution has been calculated, the pixelhaving an edge strength with a threshold value or more is detected. Inother words, since an outline including the chin lower base generallyhas a high edge strength, it becomes possible to select only a pixelwith a high possibility of corresponding to the outline including thechin lower base by selecting the pixel having the edge strength with aspecific threshold value or more and by eliminating other pixels.

Finally, the approximated curve is obtained to match the distribution ofeach of the detected pixels and the lowermost part of the approximatedcurve is identified as the lower base of the chin of the human face anddetected.

Thereby it becomes possible to detect a robust chin lower base byaccurately and quickly detecting an outline of the chin of the humanface, which is difficult to detect from a face image.

A chin detecting method for detecting a lower base of a chin of a humanface from an image with the human face included therein according toAspect 2 is characterized in that: after detecting a face image of anarea including both eyes and the lips of the human face but notincluding the chin and after setting a chin detecting window with a sizeincluding the chin of the human face at a lower part of the detectedface image, a pixel having an edge strength with a threshold value ormore is detected by calculating a primary differentiation type edgestrength distribution within the chin detecting window and bycalculating the threshold value from the primary differentiation typeedge strength distribution; then a pixel to be used is narrowed downfrom the pixels by using a sign inversion of a secondary differentiationtype edge; and thereafter an approximated curve is obtained to match adistribution of the narrowed-down pixel by using a least-square methodand a lowermost part of the approximated curve is identified as thelower base of the chin of the human face.

In the invention, there is a more specialized calculating method than inAspect 1 for the edge strength distribution (primary differentiationtype), pixel selecting method (secondary differentiation type) and forthe approximated curve (least-square method). Thereby the chin lowerbase of human face can be detected more accurately and faster than inAspect 1.

In the chin detecting method according to Aspect 1 or 2, a chindetecting method according to Aspect 3 is characterized in that the chindetecting window has a horizontally long rectangular shape, a width ofthe chin detecting window is wider than a width of the human face andthe height of the chin detecting window is shorter than the width of thehuman face.

Thereby since the chin lower base of the human face to be detected canbe reliably captured within the chin detecting window, the chin lowerbase can be detected more accurately.

In a chin detecting method according to Aspect 2 or 3, a chin detectingmethod according to Aspect 4 is characterized in that the primarydifferentiation type edge strength distribution is obtained by using aSobel edge detection operator.

Calculating differentiation with respect to a contrast is the mostrepresentative method of detecting a sharp contrast in the image. Sincea difference is substituted for the differentiation of a digital image,an edge part with a sharp contrast in the image can be effectivelydetected by primarily differentiating an original image within the chindetecting window.

In the invention, a Sobel edge detection operator excellent in detectionperformance is used as a primary differentiation type edge detectionoperator (filter). Thereby the edge part within the chin detectingwindow can be detected reliably.

In a chin detecting method according to one of Aspects 2 to 4, a chindetecting method according to Aspect 5 is characterized in that thesecondary differentiation type edge is obtained by using a Laplace edgedetection operator.

Thereby the secondary differentiation type edge can be detectedreliably.

In a chin detecting method according to one of Aspects 1 to 5, a chindetecting method according to Aspect 6 is characterized in that theapproximated curve is obtained by using a least-square method by aquadratic function.

As a method of obtaining the approximated curve within the chindetecting window which can be identified as the chin outline of thehuman face, a least-square method by a quadratic function is used in theinvention. Thereby the chin outline of the human face within the chindetecting window can be quickly obtained.

The least-square method employed in the invention is a method of findinga coefficient to minimize a sum of squares of errors from a functionwhich has tried to fit a group of plural samplings, as generallyunderstood. For example, a quadratic expression may be used forexperimental data which shows a phenomenon behaving as the quadraticexpression. When it is expected that the experimental data will show aphenomenon behaving as an exponential function, a calculation can bemade by calculating the logarithm. It is possible to easily obtain theapproximated curve by the least-square method by using software (aprogram) already incorporated in many scientific electronic calculatorsand spreadsheet software.

A chin detecting system for detecting a lower base of a chin of a humanface from an image with the human face included therein according toAspect 7 comprises: an image scanning part for scanning the image withthe human face included therein; a face detecting part for detecting anarea including both eyes and the lips of the human face but notincluding the chin from the image scanned in the image scanning part andfor setting a face detecting frame in the detected area; a chindetecting window setting part for setting a chin detecting window with asize including the chin of the human face at a lower part of thedetecting frame; an edge calculating part for calculating an edgestrength distribution within the chin detecting window; a pixelselecting part for selecting pixels having an edge strength with athreshold value or more based on the edge strength distribution obtainedby the edge calculating part; a curve approximating part for obtainingan approximated curve to match a distribution of each of the pixelsselected in the pixel selecting part; and a chin detecting part fordetecting a lowermost part of the approximated curve obtained in thecurve approximating part as the lower base of the chin of the humanface.

Thereby, as in Aspect 1, it becomes possible to detect a robust chinlower base by accurately and quickly detecting an outline of the chin ofhuman face, which is difficult to detect from a face image.

By realizing each part by using special hardware and a computer system,it becomes possible to exert these operations and effects automatically.

In a chin detecting system according to Aspect 7, a chin detectingsystem according to Aspect 8 is characterized in that the pixelselecting part detects a pixel having an edge strength with a thresholdvalue or more by calculating the threshold value from a primarydifferentiation type edge strength distribution calculated in the edgecalculating part, and then selects a pixel to be used from the pixels byusing a sign inversion of a secondary differentiation type edge.

Thereby, as in Aspects 2 and 7, it becomes possible to detect a chinlower base accurately and quickly. In addition, by realizing each partby using special hardware and a computer system, it becomes possible toexert these operations and effects automatically.

A chin detecting program for detecting a lower base of a chin of a humanface from an image with the human face included therein according toAspect 9 makes a computer realize: an image scanning part for scanningthe image with the human face included therein; a face detecting partfor detecting an area including both eyes and the lips of the human facebut not including the chin from the image scanned in the image scanningpart and for setting a face detecting frame in the detected area; a chindetecting window setting part for setting a chin detecting window with asize including the chin of the human face at a lower part of thedetecting frame; an edge calculating part for calculating an edgestrength distribution within the chin detecting window; a pixelselecting part for selecting pixels having an edge strength with athreshold value or more based on the edge strength distribution obtainedby the edge calculating part; a curve approximating part for obtainingan approximated curve to match a distribution of each of the pixelsselected in the pixel selecting part; and a chin detecting part fordetecting a lowermost part of the approximated curve obtained in thecurve approximating part as the lower base of the chin of the humanface.

Thereby since it becomes possible to obtain the same effect as inAspects 1 and 7 and to realize the function in software by using ageneral-purpose computer (hardware) such as a PC, the function can berealized more economically and easily as compared to the case ofrealizing it by creating a special apparatus. In addition, versionupgrades can be easily attained such as a change and an improvement ofthe function only by rewriting a program in many cases.

In a chin detecting program according to Aspect 9, a chin detectingprogram according to Aspect 10 is characterized in that the pixelselecting part detects a pixel having an edge strength with a thresholdvalue or more by calculating the threshold value from a primarydifferentiation type edge strength distribution calculated in the edgecalculating part, and then selects a pixel to be used from the pixels byusing a sign inversion of a secondary differentiation type edge.

Thereby since it becomes possible to obtain the same effect as inAspects 2 and 8 and to realize the function in software as in Aspect 9,the function can be realized economically and easily. In addition,version upgrades can be easily attained such as a change and animprovement of the function.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing one embodiment of a chin detectingsystem according to the invention.

FIG. 2 is a block diagram showing hardware configuring the chindetecting system.

FIG. 3 is a flowchart showing one embodiment of a chin detecting methodaccording to the invention.

FIG. 4 is a graph showing a relationship between the luminance of theface image and the pixel location thereof.

FIGS. 5(a) and 5(b) are graphs showing a relationship between the edgestrength of the face image and the pixel location thereof.

FIG. 6 is a view showing the face image from which the chin will bedetected.

FIG. 7 is a view showing the state in which a face detecting frame isset at the face image.

FIG. 8 is a view showing the state in which a chin detecting window isset at the lower part of face detecting frame.

FIGS. 9(a) and 9(b) are views showing the state in which the chin lowerbase is detected and the location is modified.

FIG. 10 is a view showing the chin detecting window in which only apixel having the edge strength with a threshold value or more isindicated.

FIG. 11 is a view showing the chin detecting window in which only thepixel selected as a result of sign inversion is indicated.

FIGS. 12(a) and 12(b) are views showing a Sobel edge detection operator.

FIG. 13 is a view showing a Laplacian filter.

DETAILED DESCRIPTION

A best mode for carrying out the invention will be described withreference to the drawings.

FIG. 1 shows one embodiment of a chin detecting system 100 for a humanface according to the invention.

As shown in this Figure, the chin detecting system 100 comprises: animage scanning part 10 for scanning a face image G with the human faceincluded therein; a face detecting part 12 for detecting the human facefrom the face image G scanned in the image scanning part 10 and forsetting a face detecting frame F of the human face; a chin detectingwindow setting part 14 for setting a chin detecting window W with a sizeincluding the chin of the human face at a lower part of the facedetecting frame F; an edge calculating part 16 for calculating an edgestrength distribution within the chin detecting window W; a pixelselecting part 18 for selecting pixels having an edge strength with athreshold value or more based on the edge strength distribution obtainedby the edge calculating part 16; a curve approximating part 20 forobtaining an approximated curve to substantially match a distribution ofeach of the pixels selected in the pixel selecting part 18; and a chindetecting part 22 for detecting a lowermost part of the approximatedcurve obtained in the curve approximating part 20 as the lower base ofthe chin of the human face.

First, the image scanning part 10 provides a function of obtaining afacial portrait for visual identification attached to, for example, apublic ID such as a passport and a driver's license or attached to aprivate ID such as an employee ID card, a student ID card and amembership card, in other words, obtaining the face image G which has nobackground and includes largely the human face facing the front asdigital image data including each pixel data of R (red), G (green) and B(blue) by using an image pickup sensor such as a CCD (Charge CoupledDevice) or a CMOS (Complementary Metal Oxide Semiconductor).

More specifically, the CCD of a digital still camera and a digital videocamera, a CMOS camera, a vidicon camera, an image scanner, a drumscanner and so on may be used. There is provided a function of analog todigital (A/D) converting the face image G optically scanned in the imagepickup sensor and sequentially sending the digital image data to theface detecting part 12.

In addition, the image scanning part 10 has a data storing function inwhich the scanned face image data can be properly stored in a storagedevice such as a hard disk drive (HDD) and in a storage medium such asDVD-ROM. When the face image is supplied as digital image data through anetwork and a storage medium, the image scanning part 10 becomesunnecessary or functions as a communication part or an interface (I/F).

Next, the face detecting part 12 provides a function of detecting thehuman face from the face image G scanned in the image scanning part 10and setting the face detecting frame F at the detected part.

This face detecting frame F has a size (an area) including both eyes andthe lips of the human face with the nose centered but not including thechin, which will be described later.

In addition, although a detection algorithm for the human face by theface detecting part 12 is not especially limited, a conventional methodcan be utilized as described in the following document, for example:

H. A. Rowley, S. Baluja and T. Kanade,

“Neural network-based face detection”

IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20,no. 1, pp. 23-38, 1998.

According to the technology described in this document, creating a faceimage of an area including both eyes and the lips of the human face butnot including the chin, and training a neural network by using thisimage, the human face is detected by using the trained neural network.According to the disclosed technology mentioned above, the area fromboth eyes to the lips is detected as a face image area.

The size of the face detecting frame F is not unchangeable and can beincreased and decreased depending on the size of the target face image.

The chin detecting window setting part 14 has a function of setting thechin detecting window W with a size including the chin of the human faceat a lower part of the face detecting frame F set in the face detectingpart 20. In other words, there is selected a target area for accuratelydetecting an outline including the chin lower base of the human face inthe following parts from the face image G by using the chin detectingwindow W.

The edge calculating part 16 provides a function of calculating an edgestrength distribution within the chin detecting window W. As will bedescribed later, the primary differentiation type edge strengthdistribution is obtained by using a Sobel edge detection operator or thelike.

The pixel selecting part 18 provides a function of selecting a pixelhaving an edge strength with a threshold value or more based on the edgestrength distribution obtained by the edge calculating part 16. As willbe described later, a candidate pixel obtained by the Sobel edgedetection operator by using a secondary differentiation filter(Laplacian filter) is narrowed down by using a sign inversion of theedge.

The curve approximating part 20 provides a function of obtaining anapproximated curve to match a distribution of each pixel selected in thepixel selecting part 18. As will be described later, the chin outline ofthe human face is obtained in a curve manner by using a least-squaremethod by a quadratic function as in the following formula.y=a×(x−x ₀)² +b  Formula 1

In this formula, y denotes the vertical coordinate, x denotes thehorizontal coordinate and x₀ denotes the horizontal center of the chindetecting window.

Calculating “a” and “b” by using this formula and a least-square method,“b” will express the chin lower base (a<0).

The chin lower base detecting part 22 provides a function of detecting alowermost part of the approximated curve obtained in the curveapproximating part 20 as the lower base of the chin of the human face.As shown in FIG. 9, the chin lower base may be expressly provided byattaching a noticeable marker M to the detected chin-lower base.

In addition, each of the parts 10 to 22 and so on configuring the chindetecting system 100 is actually realized by a computer system such as aPC which is configured by hardware in the form of a CPU, RAM and thelike and which is configured by a special computer program (software)shown in FIG. 3.

In the hardware for realizing the chin detecting system 100 as shown inFIG. 2, for example, through various internal/external buses 47 such asa processor bus, a memory bus, a system bus and an I/O bus which areconfigured by a PCI (Peripheral Component Interconnect) bus, an ISA(Industrial Standard Architecture) bus and so on, there arebus-connected to each other: a CPU (Central Processing Unit) 40 forperforming various controls and arithmetic processing; a RAM (RandomAccess Memory) 41 used for a main storage; a ROM (Read Only Memory) 42which is a read-only storage device; a secondary storage 43 such as ahard disk drive (HDD) and a semiconductor memory; an output unit 44configured by a monitor (an LCD (liquid crystal display) or a CRT(cathode-ray tube)) and so on; an input unit 45 configured by an imagepickup sensor and so on such as an image scanner, a keyboard, a mouse, aCCD (Charge Coupled Device) and a CMOS (Complementary Metal OxideSemiconductor); an I/O interface (I/F) 46; and so on.

Then, for example, various control programs and data that are suppliedthrough a storage medium such as a CD-ROM, DVD-ROM and a flexible disk(FD) and through a communication network (LAN, WAN, Internet and so on)N are installed on the secondary storage 43 and so on. At the same time,the programs and data are loaded onto the main storage 41 if necessary.According to the programs loaded onto the main storage 41, the CPU 40performs a specific control and arithmetic processing by using variousresources. The processing result (processing data) is output to theoutput unit 44 through the bus 47 and displayed. The data is properlystored and saved (updated) in the database created by the secondarystorage 43 if necessary.

A description will now be given about an example of a chin detectingmethod using the chin detecting system 100 having such a configurationwith reference to FIGS. 3-13.

FIG. 3 is a flowchart showing an example of a chin detecting method forthe face image G to be actually detected.

First, as shown in step S101, by the face detecting part 12, afterdetecting a face included in the face image G from the face image Gwhich has been scanned in the image scanning part 10 and from which thechin will be detected, the face detecting frame F for specifying thedetected human face is set.

For example, since the image from which the chin will be detected in theinvention is limited to the image of one human face as shown in FIG. 6,the location of the human face is first specified by the face detectingpart 12 and then the rectangular-shaped face detecting frame F is set onthe human face as shown in FIG. 7.

In the case of the face detecting frame F as shown in the Figure,although the face detecting frame F has a size (an area) including botheyes and the lips of the human face with the nose centered but notincluding the chin, the size and shape are not limited to thoseexemplified if the area does not include the chin part of the humanface. Also, although the human face size and the location of a displayframe Y in a horizontal direction are within the regulation with regardto each face image G shown in FIGS. 6-9(a), the chin is located too lowand is out of regulation.

Next, when the face detecting frame F has been set through the aboveprocess, moving to step S103 and setting the chin detecting window Whaving a horizontally long rectangular shape, and the chin location ofthe human face is specified.

The size and shape of the chin detecting window W is not strictlylimited. If the chin detecting window W includes an area from the lowerlip of a human face to the chin lower base without fail, the size andshape is not especially limited. However, when the chin detecting windowW is too large, there are many lines confusingly similar to the chinoutline such as the shade of the chin, the wrinkles of the neck and ashirt collar, which increases the time to detect the true edge. When thechin detecting window W is too small, the chin lower base to be detectedmay not be included in some cases due to the difference betweenindividuals.

Therefore, when using the chin detecting window having a horizontallylong rectangular shape, the width being wider than a width of the humanface and the height being shorter than the width of the human face, itis conceivable that the chin outline including the chin lower base canbe reliably captured while eliminating confusingly similar parts such asa shirt collar. Although the chin detecting window W is set bycontacting the lower side of the face detecting frame F in the exampleof FIG. 8, the chin detecting window W does not always have to contactthe face detecting frame F. It suffices if a specific positionalrelationship can be kept between the face detecting frame F and the chindetecting window W.

Next, when the chin detecting window W has been set at a target image,moving to step S105 and calculating the luminance (Y) of each pixelwithin the chin detecting window W and the primary differentiation typeedge strength distribution within the chin detecting window W isobtained based on the luminance value by using a primary differentiationtype (difference type) edge detection operator typified by a “Sobel edgedetection operator” and the like.

FIGS. 12(a) and 12(b) show this “Sobel edge detection operator”. In theoperator (filter) shown in FIG. 12(a), a horizontal edge is emphasizedby adjusting each group of three pixel values located in the left andright rows among eight pixel values surrounding a target pixel. In theoperator (filter) shown in FIG. 12(b), vertical and horizontal edges aredetected by emphasizing the vertical edge by adjusting each group ofthree pixel values located in the upper line and lower row among eightpixel values surrounding a target pixel.

After calculating the square sum of the result generated in such anoperator and calculating the square root, the edge strength can beobtained. However as described above, other primary differentiation typeedge detection operators can be applied such as “Roberts” and “Prewitt”in place of the “Sobel edge detection operator”.

FIG. 4 shows a relationship between the luminance (vertical axis) of theface image G and the pixel location (horizontal axis) thereof. Since theluminance changes sharply at the edge part in the image such as the chinoutline, a parabola-shaped approximated curve can be obtained by using aprimary differentiation type (difference type) edge detection operatorsuch as the “Sobel edge detection operator”.

Next, when the edge strength distribution within the chin detectingwindow W has been obtained in such a manner, moving to step S107, athreshold value is calculated from the edge strength distribution. Thereason for this is that, as described above, since the edge strength isgreatly affected by photographing conditions (lighting conditions) andso on, it is difficult to determine the edge corresponding to the chinoutline from the edge strength including other areas.

Although the threshold value for determining the pixels is notespecially limited, the threshold value may be set at one-tenth of themaximum edge strength detected in the chin detecting window W, forexample, and the pixels having a stronger edge than this threshold valueare selected as candidate pixels for obtaining the chin lower base.

Next, when the threshold value for sorting out the pixel values has beendetermined, moving to step S111 and selecting only the pixels having theedge strength exceeding the threshold value while scanning in a verticaldirection by setting all pixels configuring the upper side of the chindetecting window W as the base point as shown in FIG. 10, and the pixelsless than threshold value are eliminated.

FIG. 10 shows simply the pixel distribution of the pixels thus selected(exceeding the threshold value). The pixels having the edge strengthwith a threshold value or more are identified and indicated by scanningthe pixels on each line in a non-interlaced manner, that is, scanning inthe X-direction within the chin detecting window W from the upper leftof the chin detecting window W and moving to the Y-directionsequentially.

The reason for scanning from the upper left of the chin detecting windowW is that a candidate pixel with a threshold value or more appearingearliest in the Y-direction will be identified as a potential candidateof the chin lower base. Thereby it becomes possible to detect the pixelscorresponding to the chin outline effectively. In other words, thereason is that, since an edge which is confusingly similar to the chinoutline is stronger at the wrinkles of the neck and a shirt collarlocated below the actual chin outline than the edge at the upper part ofthe actual chin outline, the lower edge is desired to below-prioritized.

Next, when the pixels having the edge strength exceeding threshold valuehave been selected, moving to step S113, a sign inversion of a secondarydifferentiation type edge is detected in each row in order to narrowdown (identify) the pixel having the maximum edge strength in each pixelrow (Y-direction) among the selected pixels.

When identifying the candidate pixel, it is necessary to consider howsharply the luminance changes. When the luminance changes slowly asshown in FIG. 4, the primary differentiation type Sobel edge strengthchanges slightly and slowly as shown in FIG. 5(a). When reaching andexceeding the threshold value, the number of candidate pixels increasesto lead to an error in determining the chin lower base.

For this reason, by detecting the edge sign inversion by using asecondary differentiation type edge detection filter (Laplacian filter)as shown in FIG. 13, one pixel will be determined among plural candidatepixels in each row as shown in FIG. 10 (and FIG. 11).

For example, in the case of selecting plural pixels in each row from “a”to “g” as a result of searching the pixels having the edge strength witha threshold value or more as shown in FIG. 10, each uppermost pixel isselected as the candidate pixel configuring the chin outline in rows“a”, “b”, “d”, “f” and “g” in FIG. 11 while each lowermost pixel isselected as the candidate pixel configuring the chin outline in rows “c”and “e” in FIG. 11.

After that, when the selected candidate pixel has been finally narroweddown among many pixels exceeding the threshold value, moving to stepS115, putting the above-described approximated curve into thedistribution of the pixels searched, and the chin lower base will beobtained.

When the chin lower base has been detected, attaching a marker M to thechin lower base as shown in FIGS. 9(a) and 9(b) and the entire humanface will be moved so that the marker M located at the same location asthe proper (regulation) location of chin lower base.

In FIG. 9(a), since the chin lower base of the human face is locatedquite low, the chin lower base can be located at the regulation locationby moving the human face vertically upward as shown in FIG. 9(b).Although the image ends at the neck of human as shown in FIG. 9(a) andso on, the image under the neck is assumed to exist actually as it is.

As described above, since the lower base of the human face is detectedbased on the edge strength distribution within the chin detecting windowafter setting the chin detecting window by using a publicly-known humanface detecting method, it becomes possible to detect a robust chin lowerbase by accurately and quickly detecting the chin lower base of thehuman face, which is difficult to detect from a face image.

1. A chin detecting method for detecting a lower base of a chin of ahuman face from an image with the human face included therein, themethod comprising: detecting a face image of an area including both eyesand lips of the human face but excluding the chin; setting a chindetecting window with a size including the chin of the human face at alower part of the detected face image; detecting pixels having an edgestrength with at least a threshold value based on an edge strengthdistribution by calculating the edge strength distribution within thechin detecting window; and thereafter obtaining an approximated curve tomatch a distribution of each of the detected pixels and identifying alowermost part of the approximated curve as the lower base of the chinof the human face.
 2. A chin detecting method for detecting a lower baseof a chin of a human face from an image with the human face includedtherein, the method comprising: detecting a face image of an areaincluding both eyes and lips of the human face but excluding the chin;setting a chin detecting window with a size including the chin of thehuman face at a lower part of the detected face image; detecting pixelshaving an edge strength with at least a threshold value by calculating aprimary differentiation type edge strength distribution within the chindetecting window and by calculating the threshold value from the primarydifferentiation type edge strength distribution; identifying selectpixels to be used from the pixels by using a sign inversion of asecondary differentiation type edge; and thereafter obtaining anapproximated curve to match a distribution of the selected pixels byusing a least-square method and identifying a lowermost part of theapproximated curve as the lower base of the chin of the human face.
 3. Achin detecting method according to claim 1 wherein the chin detectingwindow has a horizontally long rectangular shape, a width of the chindetecting window is wider than a width of the human face and a height ofthe chin detecting window is shorter than a width of the human face. 4.A chin detecting method according to claim 2 wherein the primarydifferentiation type edge strength distribution is obtained by using aSobel edge detection operator.
 5. A chin detecting method according toclaim 2 wherein the secondary differentiation type edge is obtained byusing a Laplace edge detection operator.
 6. A chin detecting methodaccording to claim 1 wherein the approximated curve is obtained by usinga least-square method by a quadratic function.
 7. A chin detectingsystem for detecting a lower base of a chin of a human face from animage with the human face included therein comprising: an image scanningpart for scanning the image with the human face included therein; a facedetecting part for detecting an area including both eyes and lips of thehuman face but excluding the chin from the image scanned in the imagescanning part and for setting a face detecting frame in the detectedarea; a chin detecting window setting part for setting a chin detectingwindow with a size including the chin of the human face at a lower partof the face detecting frame; an edge calculating part for calculating anedge strength distribution within the chin detecting window; a pixelselecting part for selecting pixels having an edge strength with atleast a threshold value based on the edge strength distribution obtainedby the edge calculating part; a curve approximating part for obtainingan approximated curve to match a distribution of each of the pixelsselected in the pixel selecting part; and a chin detecting part fordetecting a lowermost part of the approximated curve obtained in thecurve approximating part as the lower base of the chin of the humanface.
 8. A chin detecting system according to claim 7 wherein the pixelselecting part detects pixels having the edge strength with at least thethreshold value by calculating the threshold value from a primarydifferentiation type edge strength distribution calculated in the edgecalculating part, and then selects certain pixels to be used from thepixels by using a sign inversion of a secondary differentiation typeedge.
 9. A chin detecting program for detecting a lower base of a chinof a human face from an image with the human face included thereinmaking a computer realize the parts of claim
 7. 10. A chin detectingprogram according to claim 9 wherein the pixel selecting part detectspixels having the edge strength with at least the threshold value bycalculating the threshold value from a primary differentiation type edgestrength distribution calculated in the edge calculating part, and thenselects certain pixels to be used from the pixels by using a signinversion of a secondary differentiation type edge.